# import the necessary packages import numpy as np import argparse import cv2 import os # construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-i", "--image", required=True, help="path to input image") ap.add_argument("-f", "--face", required=True, help="path to face detector model directory") ap.add_argument("-a", "--age", required=True, help="path to age detector model directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") args = vars(ap.parse_args())
如上所述，本文的年龄检测器是一种分类器，可以根据预定义的年龄分段，通过人的面部 ROI 预测这个人的年龄——我们不会将其视为回归问题。现在让我们定义这些年龄段的bucket：
# define the list of age buckets our age detector will predict AGE_BUCKETS = ["(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)", "(38-43)", "(48-53)", "(60-100)"]
# load the input image and construct an input blob for the image image = cv2.imread(args["image"]) (h, w) = image.shape[:2] blob = cv2.dnn.blobFromImage(image, 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and obtain the face detections print("[INFO] computing face detections...") faceNet.setInput(blob) detections = faceNet.forward()
# loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with the # prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the confidence is # greater than the minimum confidence if confidence > args["confidence"]: # compute the (x, y)-coordinates of the bounding box for the # object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # extract the ROI of the face and then construct a blob from # *only* the face ROI face = image[startY:endY, startX:endX] faceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False)
# make predictions on the age and find the age bucket with # the largest corresponding probability ageNet.setInput(faceBlob) preds = ageNet.forward() i = preds[0].argmax() age = AGE_BUCKETS[i] ageConfidence = preds[0][i] # display the predicted age to our terminal text = "{}: {:.2f}%".format(age, ageConfidence * 100) print("[INFO] {}".format(text)) # draw the bounding box of the face along with the associated # predicted age y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(image, (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(image, text, (startX, y), cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # display the output image cv2.imshow("Image", image) cv2.waitKey(0)
# import the necessary packages from imutils.video import VideoStream import numpy as np import argparse import imutils import time import cv2 import os
def detect_and_predict_age(frame, faceNet, ageNet, minConf=0.5): # define the list of age buckets our age detector will predict AGE_BUCKETS = ["(0-2)", "(4-6)", "(8-12)", "(15-20)", "(25-32)", "(38-43)", "(48-53)", "(60-100)"] # initialize our results list results = [] # grab the dimensions of the frame and then construct a blob # from it (h, w) = frame.shape[:2] blob = cv2.dnn.blobFromImage(frame, 1.0, (300, 300), (104.0, 177.0, 123.0)) # pass the blob through the network and obtain the face detections faceNet.setInput(blob) detections = faceNet.forward()
# loop over the detections for i in range(0, detections.shape[2]): # extract the confidence (i.e., probability) associated with # the prediction confidence = detections[0, 0, i, 2] # filter out weak detections by ensuring the confidence is # greater than the minimum confidence if confidence > minConf: # compute the (x, y)-coordinates of the bounding box for # the object box = detections[0, 0, i, 3:7] * np.array([w, h, w, h]) (startX, startY, endX, endY) = box.astype("int") # extract the ROI of the face face = frame[startY:endY, startX:endX] # ensure the face ROI is sufficiently large if face.shape[0] < 20 or face.shape[1] < 20: continue
请看29-43行——它们循环检测，以确保较高的置信度，然后提取出面部ROI。
第46-47行是新的——由于以下两个原因，我们要确保视频中的面部ROI足够大：
首先，我们要筛掉帧中检测到的假阳性面部。
其次，年龄分类结果对于远离相机的脸（即脸部很小）来说并不准确。
为了完成我们的辅助功能，我们将进行年龄识别并返回结果：
# construct a blob from *just* the face ROI faceBlob = cv2.dnn.blobFromImage(face, 1.0, (227, 227), (78.4263377603, 87.7689143744, 114.895847746), swapRB=False) # make predictions on the age and find the age bucket with # the largest corresponding probability ageNet.setInput(faceBlob) preds = ageNet.forward() i = preds[0].argmax() age = AGE_BUCKETS[i] ageConfidence = preds[0][i] # construct a dictionary consisting of both the face # bounding box location along with the age prediction, # then update our results list d = { "loc": (startX, startY, endX, endY), "age": (age, ageConfidence) } results.append(d) # return our results to the calling function return results
# construct the argument parse and parse the arguments ap = argparse.ArgumentParser() ap.add_argument("-f", "--face", required=True, help="path to face detector model directory") ap.add_argument("-a", "--age", required=True, help="path to age detector model directory") ap.add_argument("-c", "--confidence", type=float, default=0.5, help="minimum probability to filter weak detections") args = vars(ap.parse_args())
我们的脚本需要三个命令行参数：
face：预先训练的面部检测器模型的目录的路径
age：预先训练的年龄检测器模型的目录
confidence：最小概率阈值，以便筛除低置信检测
在这里，我们将加载模型并初始化视频：
# load our serialized face detector model from disk print("[INFO] loading face detector model...") prototxtPath = os.path.sep.join([args["face"], "deploy.prototxt"]) weightsPath = os.path.sep.join([args["face"], "res10_300x300_ssd_iter_140000.caffemodel"]) faceNet = cv2.dnn.readNet(prototxtPath, weightsPath) # load our serialized age detector model from disk print("[INFO] loading age detector model...") prototxtPath = os.path.sep.join([args["age"], "age_deploy.prototxt"]) weightsPath = os.path.sep.join([args["age"], "age_net.caffemodel"]) ageNet = cv2.dnn.readNet(prototxtPath, weightsPath) # initialize the video stream and allow the camera sensor to warm up print("[INFO] starting video stream...") vs = VideoStream(src=0).start() time.sleep(2.0)
# loop over the frames from the video stream while True: # grab the frame from the threaded video stream and resize it # to have a maximum width of 400 pixels frame = vs.read() frame = imutils.resize(frame, width=400) # detect faces in the frame, and for each face in the frame, # predict the age results = detect_and_predict_age(frame, faceNet, ageNet, minConf=args["confidence"]) # loop over the results for r in results: # draw the bounding box of the face along with the associated # predicted age text = "{}: {:.2f}%".format(r["age"][0], r["age"][1] * 100) (startX, startY, endX, endY) = r["loc"] y = startY - 10 if startY - 10 > 10 else startY + 10 cv2.rectangle(frame, (startX, startY), (endX, endY), (0, 0, 255), 2) cv2.putText(frame, text, (startX, y),
cv2.FONT_HERSHEY_SIMPLEX, 0.45, (0, 0, 255), 2) # show the output frame cv2.imshow("Frame", frame) key = cv2.waitKey(1) & 0xFF # if the `q` key was pressed, break from the loop if key == ord("q"): break # do a bit of cleanup cv2.destroyAllWindows() vs.stop()
